ABSTRACT
Objective:To investigate the value of quantitative parameters derived from dual-layer spectral detector CT (SDCT) in characterizing regional lymph node (LN) status of colorectal cancer.Methods:From August 2019 to May 2020, 101 patients with colorectal cancer confirmed by pathology in the First Affiliated Hospital of Sun Yat-sen University were retrospectively collected. The largest regional LNs were matched with surgical pathology one by one and divided into metastatic LNs group (42 cases) and nonmetastatic LNs group (59 cases) according to pathological results. Based on preoperative venous phase contrast enhanced SDCT images he short-axis diameter (S) and the of the largest regional LN was measured, then its border and enhancement homogeneity were evaluated. Outlining the ROI along the edge of the LN on its widest cross section, the iodine density (ID) and effective atomic number (Z eff) were measured, then the normalized ID (nID) and normalized Z eff (nZ eff) were calculated. The χ 2 test, Fisher′s exact test, independent samples t-test or Mann-Whitney U test were used to compare the differences of each parameter between pathologically metastatic and nonmetastatic LNs and a logistic regression model was constructed. The ROC curves and area under the curve (AUC) were performed to evaluate the diagnostic performance of each parameter. DeLong test was used to compare the differences of each AUC. Results:The S, border, enhancement homogeneity, ID, Z eff, nID and nZ eff of LNs all showed significant differences between metastatic and nonmetastatic LNs (all P<0.001). The regression model constructed by S and Z eff of LNs had the highest value in differentiating metastatic and nonmetastatic LNs, with an AUC of 0.935, sensitivity and specificity of 85.7% and 89.8%, respectively. Its diagnostic value was higher than that of S, border, enhancement homogeneity (AUC 0.674-0.832, all P<0.05) and SDCT quantitative parameters (AUC 0.863-0.906, all P<0.05) of LNs. Conclusion:SDCT quantitative parameters facilitate the accurate diagnosis of regional metastatic LNs in patients with colorectal cancer, among which the multi-parameter regression model has the highest diagnostic value.